Building Agentic AI with Control-Plane Architecture

Developing an Advanced Agentic AI

In this tutorial, we build an advanced Agentic AI using the control-plane design pattern. This involves treating the control plane as the central orchestrator that coordinates tools, manages safety rules, and structures the reasoning loop.

Implementing a Retrieval System

We set up a miniature retrieval system, define modular tools, and integrate an agentic reasoning layer that dynamically plans and executes actions. The system behaves like a disciplined, tool-aware AI capable of retrieving knowledge, assessing understanding, updating learner profiles, and logging all interactions.

import subprocess
import sys
 
def install_deps():
   deps = ['anthropic', 'numpy', 'scikit-learn']
   for dep in deps:
       subprocess.check_call([sys.executable, '-m', 'pip', 'install', '-q', dep])
 
try:
   import anthropic
except ImportError:
   install_deps()
   import anthropic
 
import json
import numpy as np
from sklearn.metrics.pairwise import cosine_similarity
from dataclasses import dataclass, asdict
from typing import List, Dict, Any, Optional
from datetime import datetime
 
@dataclass
class Document:
   id: str
   content: str
   metadata: Dict[str, Any]
   embedding: Optional[np.ndarray] = None
 
class SimpleRAGRetriever:
   def __init__(self):
       self.documents = self._init_knowledge_base()
 
   def _init_knowledge_base(self) -> List[Document]:
       docs = [...]  # Complete as per original content
       return docs
   
   def retrieve(self, query: str, top_k: int = 2) -> List[Document]:
       query_embedding = np.random.rand(128)
       scores = [...]  # Complete as per original content
       return [self.documents[i] for i in top_indices]

Building the Tool Registry

Next, we build the tool registry that our agent uses while interacting with the system, defining tools such as knowledge search, assessments, profile updates, and logging.

class ToolRegistry:
   def __init__(self, retriever: SimpleRAGRetriever):
       self.retriever = retriever
       self.interaction_log = []
       self.user_state = {...}  # Complete as per original content

Orchestrating with Control Plane

We implement the control plane that orchestrates tool execution, checks safety rules, and manages permissions, ensuring predictable and safe agentic behavior.

class ControlPlane:
   def __init__(self, tool_registry: ToolRegistry):
       self.tools = tool_registry
       self.safety_rules = {...}  # Complete as per original content

Creating the Tutor Agent

Finally, we implement the TutorAgent, which synthesizes responses by coordinating retrieval, assessment, and logging.

class TutorAgent:
   def __init__(self, control_plane: ControlPlane, api_key: str):
       self.control_plane = control_plane
       self.client = anthropic.Anthropic(api_key=api_key)
       self.conversation_history = []

Running the Demo

We run a complete demo initializing all components and processing sample student queries. As we finish, we get a clear picture of how the entire architecture works together in a realistic teaching loop.

Key Takeaways

This guide provides insights on how the control-plane pattern simplifies orchestration, strengthens safety, and creates a clean separation between reasoning and tool execution. We observe how various components of the system work together to deliver an intelligent AI tutor that responds effectively to student queries.